A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

As of April 2026, Gemma 4 tool calling is broken in Ollama v0.20.0 (ollama/ollama#15241) - the tool call parser fails and streaming drops tool calls entirely. OpenCode also has issues with local OpenAI-compatible providers (anomalyco/opencode#20669, #20719).

This guide documents a working setup using:

• llama.cpp (built from source with PR #21326 template fix + PR #21343 tokenizer fix) instead of Ollama
• OpenCode built from source with PR #16531 tool-call compatibility layer

Tested on macOS Apple Silicon (M1 Max, 32GB) on April 2, 2026.

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

Currently in no particular order. Most of these are kind of ancient.

Where's all the modern documentation? So much of what I've turned up searching is other folks complaining about having few options beyond reading source code.

The OREILLY books, while dated, seem to be some of the best available. Note that these can be read with a 7-day trial. Do this! At least get through the introduction section and first chapter of each to see if it's what you're after.

https://www.netfilter.org/

A complete architecture reference for building a Perplexity-class AI search agent

Perplexity is not a smarter model. It is a disciplined Retrieval-Augmented Generation (RAG) pipeline that treats retrieval, source ranking, and inline citation as first-class engineering concerns — not afterthoughts bolted onto a chatbot. The underlying LLMs it uses (GPT-4, Claude, Gemini, its own Sonar) are the same families everyone else has access to. What differentiates it is the orchestration layer around those models.

A competing system does not require secret prompts or proprietary models. It requires robust query analysis, hybrid retrieval (BM25 + dense), multi-layer reranking, structured prompt assembly with embedded citations, constrained LLM generation, and tight observability around citation quality and latency. This is non-trivial engineering, but it is all reproducible with off-the-shelf components.

This describes the process on how to install Zwift on Linux using Steam to manage the game.

It has been verified with the following software versions:

• Linux Kernel: 6.19.2-2
• Steam Beta Branch: Stable Client
• Steam Version: 1769025840
• Steam Linux Runtime: proton-cachyos-10.0-20260207